[1] Liu C, Li J, Li B et al. A review of cloud property retrieval algorithms and product developments for fengyun satellite spectral imagers[J]. Acta Optica Sinica, 44, 1800002(2024).

[2] He J, Yuan Q Q, Li J. Generalized spectral super-resolution for multispectral satellite imagings[J]. Acta Photonica Sinica, 52, 0210002(2023).

[3] Yang T H, Hu X Q, Xu H L et al. Radiation calibration accuracy assessment of FY-3D hyperspectral infrared atmospheric sounder based on inter-comparison[J]. Acta Optica Sinica, 39, 1130003(2019).

[4] Schott J R, Salvaggio C, Volchok W J. Radiometric scene normalization using pseudoinvariant features[J]. Remote Sensing of Environment, 26, 1-16(1988).

[5] Nielsen A A, Conradsen K, Simpson J J. Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies[J]. Remote Sensing of Environment, 64, 1-19(1998).

[6] Nielsen A A. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data[J]. IEEE Transactions on Image Processing, 16, 463-478(2007).

[7] Li X T, Ye Z Z, Ye Y M et al. A convolutional neural network-based relative radiometric calibration method[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5403611(2007).

[8] Shang H Z, Husi L T, Li M et al. Remote sensing of cloud properties based on visible-to-infrared channel observation from passive remote sensing satellites[J]. Acta Optica Sinica, 42, 0600003(2022).

[9] Sun L, Yang X, Jia S F et al. Satellite data cloud detection using deep learning supported by hyperspectral data[J]. International Journal of Remote Sensing, 41, 1349-1371(2020).

[10] Sun R X, Fan R S. Multi-feature fusion image cloud detection based on SVM[J]. Geomatics & Spatial Information Technology, 41, 153-156, 160(2018).

[11] Zhu Z, Woodcock C E. Object-based cloud and cloud shadow detection in landsat imagery[J]. Remote Sensing of Environment, 118, 83-94(2012).

[12] Zhu Z, Woodcock C E. Automated cloud, cloud shadow, and snow detection in multitemporal landsat data: an algorithm designed specifically for monitoring land cover change[J]. Remote Sensing of Environment, 152, 217-234(2014).

[13] Chen Y, Qiu Q, Qu M et al. Research on cloud detection method under arctic ice environment based on FY-3D MERSI-II[J]. Geospatial Information, 18, 10-14, 4(2020).

[14] Jia L L, Wang X Q, Wang F. Cloud detection based on band operation texture feature for GF-1 multispectral data[J]. Remote Sensing Information, 33, 62-68(2018).

[15] Wang J, Cui T X, Wang Y et al. Cloud detection for GF-5 visible-shortwave infrared advanced hyperspectral image[J]. Acta Optica Sinica, 41, 0928003(2021).

[16] Zhang S N, Zhang H, Zhang B et al. An improved fmask algorithm for cloud detection applied to hyperspectral satellite[J]. Acta Optica Sinica, 43, 2428009(2023).

[17] Ishida H, Oishi Y, Morita K et al. Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions[J]. Remote Sensing of Environment, 205, 390-407(2018).

[18] Joshi P P, Wynne R H, Thomas V A. Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8[J]. International Journal of Applied Earth Observation and Geoinformation, 82, 101898(2019).

[19] Wei J, Li Z Q, Peng Y R et al. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison[J]. Atmospheric Environment, 201, 428-440(2019).

[20] Fan X, Kong J L, Zhong Y L et al. Cloud detection of remote sensing images based on XGBoost algorithm[J]. Remote Sensing Technology and Application, 38, 156-162(2023).

[21] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[22] Yang J Y, Guo J H, Yue H J et al. CDnet: CNN-based cloud detection for remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 6195-6211(2019).

[23] Jeppesen J H, Jacobsen R H, Inceoglu F et al. A cloud detection algorithm for satellite imagery based on deep learning[J]. Remote Sensing of Environment, 229, 247-259(2019).

[24] Ronneberger O, Fischer P, Brox T, Navab N, Hornegger J, Wells W M et al. U-Net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention- MICCAI 2015, 9351, 234-241(2015).

[25] Drönner J, Korfhage N, Egli S et al. Fast cloud segmentation using convolutional neural networks[J]. Remote Sensing, 10, 1782-1805(2018).

[26] Peng L K, Liu L C, Chen X H et al. Generalization ability of cloud detection network for satellite imagery based on DeepLabv3+[J]. National Remote Sensing Bulletin, 25, 1169-1186(2021).

[27] Pang S L, Sun L, Tian Y N et al. Convolutional neural network-driven improvements in global cloud detection for landsat 8 and transfer learning on sentinel-2 imagery[J]. Remote Sensing, 15, 1706(2023).

[28] Yin M J, Wang P, Hao W L et al. Cloud detection of high-resolution remote sensing image based on improved U-Net[J]. Multimedia Tools and Applications, 82, 25271-25288(2023).

[29] Chen Z H, Li X T, Ye Y M[P]. A remote sensing image cloud detection method.

[30] Zhang H, Wang Y, Yang X. Cloud detection for satellite cloud images based on fused FCN features[J]. Remote sensing letters, 13, 683-694(2022).

[31] Guo Y, Cao X, Liu B et al. Cloud detection for satellite imagery using attention-based U-Net convolutional neural network[J]. Symmetry, 12, 1056(2020).

[32] Chen L C, Zhu Y K, Papandreou G, Ferrari V, Hebert M, Sminchisescu C et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Computer vision-ECCV 2018, 11211, 833-851(2018).

[33] Xie E Z, Wang W H, Yu Z D et al. SegFormer: simple and efficient design for semantic segmentation with transformers[EB/OL]. https:∥arxiv.org/abs/2105.15203

[34] Cao H, Wang Y Y, Chen J, Karlinsky L, Michaeli T, Nishino K. et al. Swin-Unet: Unet-like pure transformer for medical image segmentation[M]. Computer vision-ECCV 2022 workshops, 13803, 205-218(2023).

[35] Liu Z, Mao H Z, Wu C Y et al. A ConvNet for the 2020s[C], 11966-11976.

[36] Li X, Yang X F, Li X T et al. GCDB-UNet: a novel robust cloud detection approach for remote sensing images[J]. Knowledge-Based Systems, 238, 107890(2022).

[37] Kumthekar A, Reddy G R. An integrated deep learning framework of U-Net and inception module for cloud detection of remote sensing images[J]. Arabian Journal of Geosciences, 14, 1900-1912(2021).

[38] Yao X D, Guo Q, Li A. Light-weight cloud detection network for optical remote sensing images with attention-based DeepLabv3+ architecture[J]. Remote Sensing, 13, 3617-3639(2021).

[39] Wang Y, Zhou Q, Liu J et al. Lednet: a lightweight encoder-decoder network for real-time semantic segmentation[C], 1860-1864.

[40] Yu C Q, Wang J B, Peng C, Ferrari V, Hebert M, Sminchisescu C et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[M]. Computer vision-ECCV 2018, 11217, 334-349(2018).

[41] Poudel R P K, Liwicki S, Cipolla R. Fast-SCNN: fast semantic segmentation network[EB/OL]. http:∥arxiv.org/abs/1902.04502v1

[42] Li J, Wu Z C, Hu Z W et al. A lightweight deep learning-based cloud detection method for sentinel-2A imagery fusing multiscale spectral and spatial features[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5401219(2021).

[43] Germann U, Zawadzki I. Scale dependence of the predictability of precipitation from continental radar images. part II: probability forecasts[J]. Journal of Applied Meteorology, 43, 74-89(2004).

[44] Cheung P, Yeung H Y. Application of optical-flow technique to significant convection nowcast for terminal areas in Hong Kong[C], 6-10(2012).

[45] Sakaino H. Spatio-temporal image pattern prediction method based on a physical model with time-varying optical flow[J]. IEEE Transactions on Geoscience and Remote Sensing, 51, 3023-3036(2013).

[46] Shi X J, Chen Z R, Wang H et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting[EB/OL]. https:∥arxiv.org/abs/1506.04214

[47] Shi X J, Gao Z H, Lausen L et al. Deep learning for precipitation nowcasting: a benchmark and a new model[EB/OL]. http:∥arxiv.org/abs/1706.03458v2

[48] Wang Y B, Long M S, Wang J M et al. PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs[EB/OL]. https:∥arxiv.org/abs/2103.09504

[49] Wang Y B, Gao Z F, Long M S et al. PredRNN++: towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning[EB/OL]. http:∥arxiv.org/abs/1804.06300v2

[50] Wang Y B, Zhang J J, Zhu H Y et al. Memory in memory: a predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics[C], 9146-9154.

[51] Wu H X, Yao Z Y, Wang J M et al. MotionRNN: a flexible model for video prediction with spacetime-varying motions[C], 15430-15439.

[52] Miyato T, Kataoka T, Koyama M et al. Spectral normalization for generative adversarial networks[EB/OL]. http:∥arxiv.org/abs/1802.05957v1

[53] le Guen V, Thome N. Disentangling physical dynamics from unknown factors for unsupervised video prediction[C], 11471-11481.

[54] Pathak J, Subramanian S, Harrington P et al. FourCastNet: a global data-driven high-resolution weather model using adaptive Fourier neural operators[EB/OL]. http:∥arxiv.org/abs/2202.11214v1

[55] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial networks[EB/OL]. http:∥arxiv.org/abs/1406.2661v1

[56] Tian L, Li X T, Ye Y M et al. A generative adversarial gated recurrent unit model for precipitation nowcasting[J]. IEEE Geoscience and Remote Sensing Letters, 17, 601-605(2020).

[57] Xie P F, Li X T, Ji X Y et al. An energy-based generative adversarial forecaster for radar echo map extrapolation[J]. IEEE Geoscience and Remote Sensing Letters, 19, 3500505(2020).

[58] Ravuri S, Lenc K, Willson M et al. Skilful precipitation nowcasting using deep generative models of radar[J]. Nature, 597, 672-677(2021).

[59] Zhang Y C, Long M S, Chen K Y et al. Skilful nowcasting of extreme precipitation with NowcastNet[J]. Nature, 619, 526-532(2023).

[60] Dai K, Li X T, Ye Y M et al. MSTCGAN: multiscale time conditional generative adversarial network for long-term satellite image sequence prediction[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 4108516(2022).

[61] Dai K, Li X T, Ma C et al. Learning spatial-temporal consistency for satellite image sequence prediction[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 4104517(2023).

[62] Gao Z H, Shi X J, Han B R et al. PreDiff: precipitation nowcasting with latent diffusion models[EB/OL]. http:∥arxiv.org/abs/2307.10422v2

[63] Zhou L, Zhang R H. A self-attention-based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions[J]. Science Advances, 9, eadf2827(2023).

[64] Yu D M, Li X T, Ye Y M et al. DiffCast: a unified framework via residual diffusion for precipitation nowcasting[EB/OL]. http:∥arxiv.org/abs/2312.06734v2

[65] Ushio T, Sasashige K, Kubota T et al. A Kalman filter approach to the global satellite mapping of precipitation (GSMaP) from combined passive microwave and infrared radiometric data[J]. Journal of the Meteorological Society of Japan Ser II, 87A, 137-151(2009).

[66] Kwon Y, Forman B A, Ahmad J A et al. Exploring the utility of machine learning-based passive microwave brightness temperature data assimilation over terrestrial snow in high Mountain Asia[J]. Remote Sensing, 11, 2265-2293(2019).

[67] Derin Y, Bhuiyan M A E, Anagnostou E et al. Modeling level 2 passive microwave precipitation retrieval error over complex terrain using a nonparametric statistical technique[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 9021-9032(2021).

[68] Xiao X X, He T, Liang S L et al. Improving fractional snow cover retrieval from passive microwave data using a radiative transfer model and machine learning method[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 4304215(2021).

[69] Liu K W, He J Y, Chen H N. Precipitation retrieval from Fengyun-3D microwave humidity and temperature sounder data using machine learning[J]. Remote Sensing, 14, 848(2022).

[70] Das S, Wang Y D, Gong J et al. A comprehensive machine learning study to classify precipitation type over land from global precipitation measurement microwave imager (GPM-GMI) measurements[J]. Remote Sensing, 14, 3631(2022).

[71] Lazri M, Labadi K, Brucker J M et al. Improving satellite rainfall estimation from MSG data in Northern Algeria by using a multi-classifier model based on machine learning[J]. Journal of Hydrology, 584, 124705(2020).

[72] Hirose H, Shige S, Yamamoto M K et al. High temporal rainfall estimations from himawari-8 multiband observations using the random-forest machine-learning method[J]. Journal of the Meteorological Society of Japan, 97, 689-710(2019).

[73] Chen H N, Chandrasekar V, Cifelli R et al. A machine learning system for precipitation estimation using satellite and ground radar network observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 982-994(2020).

[74] Mugnai A, Smith E A, Tripoli G J et al. CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations[J]. Natural Hazards and Earth System Sciences, 13, 887-912(2013).

[75] Sanò P, Panegrossi G, Casella D et al. The Passive microwave neural network precipitation retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies[J]. Atmospheric Measurement Techniques, 8, 837-857(2015).

[76] Sanò P, Panegrossi G, Casella D et al. The new passive microwave neural network precipitation retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars[J]. Atmospheric Measurement Techniques, 9, 5441-5460(2016).

[77] Chen H N, Sun L Y, Cifelli R et al. Deep learning for bias correction of satellite retrievals of orographic precipitation[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 4104611(2022).

[78] Wang L P, Chen H N, Liao W T. Ensemble learning for improving satellite retrievals of orographic precipitation[C], 4658-4661.

[79] Wang C G, Tang G Q, Xiong W T et al. Infrared precipitation estimation using convolutional neural network for FengYun satellites[J]. Journal of Hydrology, 603, 127113(2021).

[80] Wang C G, Xu J, Tang G Q et al. Infrared precipitation estimation using convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 8612-8625(2020).

[81] Yi L, Gao Z Y, Shen Z H et al. Precipitation estimation based on infrared data with a spherical convolutional neural network[J]. Journal of Hydrometeorology, 24, 743-760(2023).

[82] Nguyen P, Shearer E J, Ombadi M et al. PERSIANN dynamic infrared-rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation[J]. Bulletin of the American Meteorological Society, 101, E286-E302(2020).

[83] Wang Z Y, Li X T, Lin K H et al. Multiscale and multilevel feature fusion network for quantitative precipitation estimation with passive microwave[J]. IEEE Transactions on Geoscience and Remote Sensing, 62, 4205916(2024).

[84] Fors A S, Brekke C, Doulgeris A P et al. Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band[J]. The Cryosphere, 10, 401-415(2016).

[85] Cristea A, van Houtte J, Doulgeris A P. Integrating incidence angle dependencies into the clustering-based segmentation of SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2925-2939(2020).

[86] Xu Y, Scott K A. Sea ice and open water classification of sar imagery using cnn-based transfer learning[C], 3262-3265.

[87] Li J X, Wang C, Wang S G et al. Gaofen-3 sea ice detection based on deep learning[C], 933-939(2017).

[88] Wang C, Zhang H, Wang Y Y et al. Sea ice classification with convolutional neural networks using sentinel-L scansar images[C], 7125-7128.

[89] Gao Y H, Gao F, Dong J Y et al. Transferred deep learning for sea ice change detection from synthetic-aperture radar images[J]. IEEE Geoscience and Remote Sensing Letters, 16, 1655-1659(2019).

[90] Boulze H, Korosov A, Brajard J. Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks[J]. Remote Sensing, 12, 2165(2020).

[91] Wang Y R, Li X M. Arctic sea ice cover data from spaceborne SAR by deep learning[EB/OL]. https:∥www.researchgate.net/publication/347316552_Arctic_sea_ice_cover_data_from_spaceborne_SAR_by_deep_learning

[92] Zhao J, Chen L, Li J et al. Semantic segmentation of sea ice based on U-Net network modification[C](2022). https:∥pdfs.semanticscholar.org/e9cd/0fcb366f3249b984865950f1518f3d27001b.pdf

[93] Malmgren-Hansen D, Pedersen L T, Nielsen A A et al. A convolutional neural network architecture for sentinel-1 and AMSR2 data fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 1890-1902(2021).

[94] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. http:∥arxiv.org/abs/1706.05587v3

[95] Balasooriya N, Dowden B, Chen J et al. In-situ sea ice detection using DeepLabv3 semantic segmentation[C], 1-7(2021).

[96] Ren Y B, Li X F, Yang X F et al. Development of a dual-attention U-net model for sea ice and open water classification on SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 19, 4010205(1068).

[97] Yu H, Tian Z X, Li C H. Sea ice classification from Sentinel-1 data[C], 790-793.

[98] Sudakow I, Asari V K, Liu R X et al. MeltPondNet: a swin transformer U-net for detection of melt ponds on Arctic Sea ice[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 8776-8784(2022).

[99] Ristea N C, Anghel A, Datcu M. Sea ice segmentation from SAR data by convolutional transformer networks[C], 168-171.

[100] Zhang Z, Deng G B, Luo C Y et al. A multiscale dual attention network for the automatic classification of polar sea ice and open water based on sentinel-1 SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 5500-5516(2024).

[101] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778.

[102] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269.

[103] Chen J N, Lu Y Y, Yu Q H et al. TransUNet: transformers make strong encoders for medical image segmentation[EB/OL]. http:∥arxiv.org/abs/2102.04306v1

[104] Guo M H, Lu C Z, Liu Z N et al. Visual attention network[J]. Computational Visual Media, 9, 733-752(2023).